Behavior prediction based on a Commodity Utility-Behavior Sequence model
With the rise of social e-commerce and live broadcast e-commerce, real-time recommendations based on consumers’ browsing behavior are becoming more and more important. Traditional utility recommendation has some problems such as cold start and subjectivity. And traditional sequential recommendation...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022000342 |
| _version_ | 1828352880728342528 |
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| author | Li Chen Hui Zhu |
| author_facet | Li Chen Hui Zhu |
| author_sort | Li Chen |
| collection | DOAJ |
| description | With the rise of social e-commerce and live broadcast e-commerce, real-time recommendations based on consumers’ browsing behavior are becoming more and more important. Traditional utility recommendation has some problems such as cold start and subjectivity. And traditional sequential recommendation relies on behavior sequences. However, these data contain some irrelevant data of products, which will lead to wrong dependencies and affect the accuracy of recommendation. To solve such problems, this research builds a Commodity Utility-Behavior Sequence (CUBS) dual-utility model. CUBS consists of two models: Commodity Utility (CU) and Behavior Sequence (BS). The Commodity Utility can evaluate the psychological motivation of consumer and transform it into commodity utility. The Behavior Sequence can predict preference by calculating the behavior sequences. CUBS combines the advantages of the Commodity Utility and Behavior Sequence. At the same time, it makes up for the shortcomings of only using a single model. A total of 22,417 records of 214 customers are randomly selected from the JD.com database as the test set. These customers are divided into 4 groups and calculated by CU, BS and CUBS respectively. The results show that the accuracy of CUBS model is the highest. This also confirms that customers’ click behaviors and behavior sequences have an important influence on purchase intention prediction. |
| first_indexed | 2024-04-14T01:59:28Z |
| format | Article |
| id | doaj.art-e5c38c0510ca4734a88ab551226e4752 |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-14T01:59:28Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-e5c38c0510ca4734a88ab551226e47522022-12-22T02:18:52ZengElsevierMachine Learning with Applications2666-82702022-09-019100314Behavior prediction based on a Commodity Utility-Behavior Sequence modelLi Chen0Hui Zhu1School of College of Finance and Business, Guangdong Vocational Institute of Public Administration(Guangdong Youth Vocational College), Guangzhou 510000, ChinaSchool of Management, Guangzhou University, Guangzhou 510006, China; Correspondence to: No. 230 Waihuan West Road, University Town, Guangzhou, Guangdong, China.With the rise of social e-commerce and live broadcast e-commerce, real-time recommendations based on consumers’ browsing behavior are becoming more and more important. Traditional utility recommendation has some problems such as cold start and subjectivity. And traditional sequential recommendation relies on behavior sequences. However, these data contain some irrelevant data of products, which will lead to wrong dependencies and affect the accuracy of recommendation. To solve such problems, this research builds a Commodity Utility-Behavior Sequence (CUBS) dual-utility model. CUBS consists of two models: Commodity Utility (CU) and Behavior Sequence (BS). The Commodity Utility can evaluate the psychological motivation of consumer and transform it into commodity utility. The Behavior Sequence can predict preference by calculating the behavior sequences. CUBS combines the advantages of the Commodity Utility and Behavior Sequence. At the same time, it makes up for the shortcomings of only using a single model. A total of 22,417 records of 214 customers are randomly selected from the JD.com database as the test set. These customers are divided into 4 groups and calculated by CU, BS and CUBS respectively. The results show that the accuracy of CUBS model is the highest. This also confirms that customers’ click behaviors and behavior sequences have an important influence on purchase intention prediction.http://www.sciencedirect.com/science/article/pii/S2666827022000342Commodity utilityBehavior sequenceImplicit feedbackBehavior prediction |
| spellingShingle | Li Chen Hui Zhu Behavior prediction based on a Commodity Utility-Behavior Sequence model Machine Learning with Applications Commodity utility Behavior sequence Implicit feedback Behavior prediction |
| title | Behavior prediction based on a Commodity Utility-Behavior Sequence model |
| title_full | Behavior prediction based on a Commodity Utility-Behavior Sequence model |
| title_fullStr | Behavior prediction based on a Commodity Utility-Behavior Sequence model |
| title_full_unstemmed | Behavior prediction based on a Commodity Utility-Behavior Sequence model |
| title_short | Behavior prediction based on a Commodity Utility-Behavior Sequence model |
| title_sort | behavior prediction based on a commodity utility behavior sequence model |
| topic | Commodity utility Behavior sequence Implicit feedback Behavior prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022000342 |
| work_keys_str_mv | AT lichen behaviorpredictionbasedonacommodityutilitybehaviorsequencemodel AT huizhu behaviorpredictionbasedonacommodityutilitybehaviorsequencemodel |